15 ML libraries every developer should know

TensorFlow

Language: Python, C++, CUDA
Use case: deep learning and scalable model production

TensorFlow is one of the most popular libraries, supported by Google, offering tools for visualization, debugging, and model deployment, making it the number one choice for industrial projects. It is useful for building and deploying complex neural networks with the ability to scale across different devices. 

Pandas

Language: Python
Use case: management and analysis of tabular data

Pandas is an indispensable tool for Data Scientists, enabling quick data manipulation and preparation for further analysis and model building. It is useful for convenient work with tabular data formats—cleaning, transformation, and visualization.

PyTorch

Language: Python, C++
Use case: deep learning with dynamic computational graphs

PyTorch is distinguished by its user-friendly and intuitive API, making it popular among both beginners and experts. It supports GPU training and integrates easily with other Python tools. It is useful for research tasks and prototyping, where flexibility and simplicity are important.

Armadillo

Language: C++
Use case: linear algebra and scientific computing

Armadillo provides a convenient interface similar to MATLAB, allowing easy work with matrices and vectors. Thanks to optimization for multithreading, the library is suitable for processing large volumes of data and high-performance computations. It is ideal for projects related to bioinformatics, computer vision, statistics, and econometrics. 

FANN, Fast Artificial Neural Network

Language: C
Use case: creation of multilayer artificial neural networks

FANN is a lightweight library for creating and training neural networks, supporting both dense and sparse connections. Thanks to a wide range of language bindings and graphical interfaces, it is suitable for research projects in biology, aviation, and pattern recognition. It is useful for fast and simple development of neural networks with the ability to train various topologies.

Keras

Language: Python
Use case: deep learning

Keras runs on top of popular computing platforms: TensorFlow, Theano, and others, allowing models to run on CPU and GPU. The simplicity and intuitiveness of its API make it an excellent choice for beginners and professionals in deep learning. It is useful for rapid prototyping and training neural networks of any complexity, including convolutional and recurrent networks.

Matplotlib

Language: Python
Use case: data visualization

Matplotlib is a universal tool for creating various types of plots. It allows for quick creation of clear diagrams with a high degree of customization, helping to better understand and interpret data. It is useful for creating high-quality graphs, charts, and visualizations necessary for result analysis and presentations.

mlpack

Language: C++
Use case: fast and scalable implementation of ML algorithms

Built on Armadillo, mlpack offers a wide range of algorithms from clustering to neural networks. It supports multiple programming languages and is convenient for integration into large projects. It is useful for developers who need high performance and flexibility when implementing machine learning algorithms.

NLTK, Natural Language Toolkit

Language: Python
Use case: natural language processing

This is a powerful set of tools for text analysis and processing. NLTK is widely used in scientific research and educational purposes, helping to quickly implement text processing projects. It is useful for solving NLP tasks—tokenization, lemmatization, syntactic parsing, and named entity recognition.

NumPy

Language: Python
Use case: scientific computing and multidimensional array processing

NumPy is the foundation for most Python libraries in ML, providing convenient data structures and operations on them. It is particularly useful for working with matrices, linear algebra, and random variables. It is useful for fast and efficient mathematical operations on large volumes of numerical data.

OpenNN

Language: C++
Use case: development and deployment of neural networks and analytics

OpenNN combines the capabilities of deep learning and statistical methods with multithreading support, allowing efficient work with large volumes of data. It is useful for high-performance solutions in predictive analytics, especially in engineering and science.

Scikit-Learn

Language: Python, C/C++
Use case: building and training classical ML models

Scikit-Learn offers a variety of algorithms for classification, regression, clustering, and data processing. It is distinguished by its ease of use and broad community support. It is useful for quickly creating, evaluating, and comparing machine learning models.

SciPy

Language: Python, C, C++, Fortran
Use case: technical and scientific computing

SciPy extends the functionality of NumPy, providing many specialized tools that are useful for scientific research and engineering tasks. It is useful for solving complex mathematical problems—integrals, optimization, signal processing.

Shogun

Language: C++
Use case: implementation of machine learning algorithms, focusing on kernel methods

Shogun offers a wide range of algorithms, integrates easily with various programming languages, and is excellent for educational purposes and prototyping. It is useful for training and research in the field of classification and regression using kernel methods.

Theano

Language: Python, CUDA
Use case: optimization and acceleration of mathematical computations

Theano allows for optimizing computations and using GPUs for acceleration. Although development has been suspended, many of Theano's ideas have formed the basis of modern libraries. It is useful for efficient computation of complex expressions and gradients, especially when working with neural networks.